We performed a second optimization that was identical to the first except that cell cycle length was fixed both in space and in time, and constrained total MZ cell number to be no more than the optimum for optimization 1 (359 cells); the minimal pedigree depth was 13.94 (Table 1, optimization 2).
Unpublished research (Nehm, unpublished data) suggests that Sequential Minimal Optimization (SMO) (Platt [1999]) is the most effective algorithm for the corpus used in EvoGrader.
One classifier, which is referred to as SMO, was adopted herein as the classification method; it implements John Platt's sequential minimal optimization algorithm to solve the optimization problem that should be settled during training of a support vector classifier.
A consequence of the use of the new formulation is that the standard optimization method employed in (epsilon -SVR, sepsilon -SVRnimal optimization (sequentialminimaleasible toptimizationheSMOnew models.
However, these were not tested in this study, as I was specifically interested in identifying gene regions that were successfully amplified under standard conditions for high-throughput processing with minimal optimization.
Sequential minimal optimization.
The optimization problem can be solved using the sequential minimal optimization technique as in standard SVM.
For the SVM [21], we used Weka's [22] implementation of Sequential Minimal Optimization (SMO) [23].
For inducing the classifiers, we have opted for Platt's [22] Sequential Minimal Optimization (SMO) algorithm.
Platt's sequential minimal optimization (SMO) [19] has been widely used for solving the SVM problem.
